Your inspection AI hits 97% accuracy in the lab. On the press line it rejects 14% of good parts. The model was never the problem. A vision model catches burrs and short fills at 97% in controlled LED lighting. Put it on a 200-ton stamping press at 40 strokes a minute and false rejects climb to double digits — the sheet metal reflects the overhead bay lights differently at each stroke angle, lubricant pools differently on a warm die than a cold one, and the first 50 parts of a shift aren't at thermal equilibrium. The fix isn't a better model. It's polarized backlighting to kill specular reflection, a thermal camera tied to die temperature, and a training set spanning cold-start, mid-run and end-of-run conditions. Then the real work starts — the part we get hired for: mapping the inspection result to the PLC over EtherNet/IP so the reject actuator fires inside the stroke window, tagging each part in the MES for traceability, routing defect images to QA by defect class and die station. Integration is ~60% of the timeline. Model training is ~15%. The hardware is a purchase order. This is why 84% of integration projects fail or partially fail — and it's never the inference speed. Only 34% of manufacturers have real-time data streaming. If your historian logs every 5 seconds but your reject decision needs 50ms, no amount of edge compute closes that gap. Predictive maintenance has the same trap: a 5% false-positive rate across 2,000 assets is 100 needless work orders a cycle — techs learn the alarm cries wolf and stop responding. Ford hit 2.5%, catching 22% of failures ~10 days early. A tuned vision pipeline takes out-of-box AOI (automated optical inspection) false rejects from 5–15% under 2% — through OPC-UA connectivity, calibration and fleet operations, not a faster Jetson. Save this before your next vision-inspection pilot — and send it to whoever's scoping the budget. #EdgeAI #MachineVision #SmartManufacturing #IndustrialAutomation #PredictiveMaintenance